The Justice Department has charged three current or former members of the so-called Syrian Electronic Army for computer hacking-related conspiracies. The charges were unsealed Tuesday in the U.S. Eastern District Court of Virginia. Prosecutors allege that two Syrians, 22-year-old Ahmad Umar Agha and 27-year-old Firas Dardar, tricked email users to steal usernames and passwords to compromise government, media and private-sector computer systems. In April 2013 they allegedly tweeted from the Associated Press account on Twitter falsely claiming a bomb had exploded at the White House and injured the president. None of them are in custody.

Boiteux, an assistant fire chief from Los Angeles, is helping train Greek first responders to use Emily. Boiteux, an assistant fire chief from Los Angeles, is helping train Greek first responders to use Emily. On a cold, rainy morning a few weeks ago, eight black inflatable rafts, loaded with migrants, bob in the waters off the northern shore of the Greek island of Lesbos. "This boat up there?" he says. So they ask for help from the coast guard." A Norwegian rescue boat with the European Union's border agency, Frontex, heads toward the distressed raft. Hantzopoulos walks along the rocky shore with John Sims, a fire captain from Sahuarita, Ariz. He's teaching members of the Hellenic Red Cross how to use a remote-controlled rescue device called Emily -- which stands for Emergency Integrated Lifesaving Lanyard. You might call Emily a buoy. You might call her a boat. She's about 4 feet long, weighs 25 pounds and looks like a cylinder wrapped in an orange-red life jacket. Sims steers Emily in the water with a remote control. She speeds toward the migrant rafts. "I'll keep her about 20-30 meters behind [them]," he says. The only thing that affects her sometimes over a wave is a little bit of wind. In a high wind situation we would actually fill the hull with some water to be able to weight her down some so, so, she wouldn't fly so bad off the top of the waves."

This article was first published in the April 2016 issue of WIRED magazine. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online. In November 2013, more than 100 patients with cancer - including pancreatic, breast, liver and brain tumours - embarked on clinical trials involving BPM 31510, a drug discovered by an algorithm. The story of BPM 31510 begins with the extraction of biological data from healthy and cancerous tissue samples from over 1,000 patients. This data was then processed by artificial intelligence algorithms, which analysed it and suggested possible drug treatments. "We've essentially reversed the scientific method," says Niven R Narain, the 38-year-old president and co-founder of Berg, the Boston pharma startup which makes BPM 31510. "Instead of a preconceived hypothesis that leads us to do experiments and generate a particular type of data, we allowed the biological data from the patients to lead us to the hypotheses." Making an effective cancer-fighting drug is a notoriously difficult process: according to Narain, development and production can cost pharmaceutical companies up to 2.6 billion ( 1.8bn) and take 12 to 14 years to complete. "Only one per cent of the cancer drugs that make it to clinical trials prove to be effective. It's expensive and the development process is inexcusably long," Narain says.

PARIS â We've hardly made peace with the idea of driverless cars, and now we're being told that artificial intelligence could also control rifles, missiles and bombs of the future. The warning was issued last summer by leading figures such as Tesla chief Elon Musk, physicist Stephen Hawking and MIT Professor Noam Chomsky. "Starting a military AI arms race is a bad idea, and should be prevented by a ban on offensive autonomous weapons beyond meaningful human control," it read. The letter had the merit of publicizing a debate that has been agitating diplomats, the defense industry and NGOs for several years, but without any major impact until now. Given the recent progress of robotics and artificial intelligence, their use in the world of weapons is not a matter of Terminator-like science fiction anymore.

"Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten." These words, articulated by Neil Armstrong at a speech to a joint session of Congress in 1969, fit squarely into most every decade since the turn of the century, and it seems to safe to posit that the rate of change in technology has accelerated to an exponential degree in the last two decades, especially in the areas of artificial intelligence and machine learning. Artificial intelligence is making an extreme entrance into almost every facet of society in predicted and unforeseen ways, causing both excitement and trepidation. This reaction alone is predictable, but can we really predict the associated risks involved?

Is there anyone working on either a decentralized deep learning algorithm, or a consumer facing app that uses AI to help people diagnose themselves? My wife was just diagnosed with CVID a couple of weeks ago, it's like AIDS except it's not Aquired, it's part genetic and part environmental - but it's a rare primary immunodeficiency disease. She's had this her entire life. She was misdiagnosed 3 or 4 times, most recently she was eating gluten free for the last 8 years because she was diagnosed as celiac disease. She's lost most of her hair over the last 6 months and has been in the hospital 3-4 times this year.

Tamr's platform, which combines machineMapping Startup Leverages Machine Vision. Read more ... » scientists, can prepare and unify these large, disparate …

A big data analytics platform is being deployed by the Ministry of Defence as it looks to take the weight of military analysts and improve threat identification. The analytics platform, which incorporates machine learning technology, will be used for operational support and comes from a recently signed contract by the Defence Science and Technology Laboratory's Centre for Defence Enterprise (CDE) and Massive Analytic, a London based company. The idea behind the platform from Massive Analytic is that it will help make it possible to examine outcomes of decisions before they are put into effect, effectively acting as a trusted advisor. Under the deployment the MoD will use the system to bring together surveillance data from static and dynamic sources which will feed an interactive dashboard. The dashboard will be capable of providing insights that will help to identify threats in advance and can be used for planning different scenarios. Military analysts are currently the main source for sifting through large volumes of data, it is hoped that this platform will help to remove some of that burden.

Being an analyst working on statistical analysis, more often than none, there is almost always a need to "explain" how did you come up with the conclusion with the techniques applied? The most demanding part of all this is: " you must explain this like I am 5 years old." Oki, so let me try to explain one machine learning algorithm called "randomforest" for a classification problem that is recognised as a black-box algorithm! Let's get started with a metaphor that hopefully is as close to everyday life as possible... In a congress, say that there are 100 members in total, they need to vote to decide whether they are going to pass a new law or not.

The video does not say that if Atlas comes after you, it will find you and kill you. Your own paranoid mind made that bit up for itself. But Courtney Hohne, a PR person at the corporate parent of Boston Dynamics, which made Atlas, wrote an internal email that described it as "terrifying, ready to take humans' jobs": We're not going to comment on this video because there's really not a lot we can add, and we don't want to answer most of the Qs it triggers. Here's a snatch of the video she was talking about: Google is now planning to sell Boston Dynamics, the company under Google-parent Alphabet that makes military robots, because they aren't commercial enough. "We don't want to answer most of the Qs it triggers" could mean a lot of different things -- most likely that it is difficult to get across the benefit of a product that can do a bunch of things humans can do when the media atmosphere is filled with humans worrying about machines taking their jobs (robots don't steal jobs, by the way).